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- Liu, X., et al.
(författare)
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Toward a minimal representation of aerosols in climate models : description and evaluation in the Community Atmosphere Model CAM5
- 2012
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Ingår i: Geoscientific Model Development. - : Copernicus GmbH. - 1991-959X .- 1991-9603. ; 5:3, s. 709-739
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Tidskriftsartikel (refereegranskat)abstract
- A modal aerosol module (MAM) has been developed for the Community Atmosphere Model version 5 (CAM5), the atmospheric component of the Community Earth System Model version 1 (CESM1). MAM is capable of simulating the aerosol size distribution and both internal and external mixing between aerosol components, treating numerous complicated aerosol processes and aerosol physical, chemical and optical properties in a physically-based manner. Two MAM versions were developed: a more complete version with seven lognormal modes (MAM7), and a version with three lognormal modes (MAM3) for the purpose of long-term (decades to centuries) simulations. In this paper a description and evaluation of the aerosol module and its two representations are provided. Sensitivity of the aerosol lifecycle to simplifications in the representation of aerosol is discussed. Simulated sulfate and secondary organic aerosol (SOA) mass concentrations are remarkably similar between MAM3 and MAM7. Differences in primary organic matter (POM) and black carbon (BC) concentrations between MAM3 and MAM7 are also small (mostly within 10 %). The mineral dust global burden differs by 10 % and sea salt burden by 30-40 % between MAM3 and MAM7, mainly due to the different size ranges for dust and sea salt modes and different standard deviations of the log-normal size distribution for sea salt modes between MAM3 and MAM7. The model is able to qualitatively capture the observed geographical and temporal variations of aerosol mass and number concentrations, size distributions, and aerosol optical properties. However, there are noticeable biases; e.g., simulated BC concentrations are significantly lower than measurements in the Arctic. There is a low bias in modeled aerosol optical depth on the global scale, especially in the developing countries. These biases in aerosol simulations clearly indicate the need for improvements of aerosol processes (e.g., emission fluxes of anthropogenic aerosols and precursor gases in developing countries, boundary layer nucleation) and properties (e.g., primary aerosol emission size, POM hygroscopicity). In addition, the critical role of cloud properties (e. g., liquid water content, cloud fraction) responsible for the wet scavenging of aerosol is highlighted.
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| 7. |
- Li, F., et al.
(författare)
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Unsupervised context-aware user preference mining
- 2013
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Ingår i: Proceeding of Workshop on Activity Context-Aware System Architectures at the 27th AAAI Conference on Artificial Intelligence. - 9781577356165 ; , s. 36-43
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Konferensbidrag (refereegranskat)abstract
- In pervasive environments, users are situated in rich context and can interact with their surroundings through various services. To improve user experience in such environments, it is essential to find the services that satisfies user preferences in certain context. Thus the suitability of discovered services is highly dependent on how much the context-aware system can understand users' current context and preferred activities. In this paper, we propose an unsupervised learning solution for mining user preferences from the user's past context. To cope with the high dimensionality and heterogeneity of context data, we propose a subspace clustering approach that is able to find user preferences identified by different feature sets. The results of our approach are validated by a series of experiments.
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- Rasch, Katharina, et al.
(författare)
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Automatic description of context-altering services through observational learning
- 2012
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Ingår i: Pervasive Computing. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642312045 ; , s. 461-477
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Konferensbidrag (refereegranskat)abstract
- Understanding the effect of pervasive services on user context is critical to many context-aware applications. Detailed descriptions of context-altering services are necessary, and manually adapting them to the local environment is a tedious and error-prone process. We present a method for automatically providing service descriptions by observing and learning from the behavior of a service with respect to its environment. By applying machine learning techniques on the observed behavior, our algorithms produce high quality localized service descriptions. In a series of experiments we show that our approach, which can be easily plugged into existing architectures, facilitates context-awareness without the need for manually added service descriptions.
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| 9. |
- Rasch, Katharina, 1982-, et al.
(författare)
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Context-driven personalized service discovery in pervasive environments
- 2011
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Ingår i: World wide web (Bussum). - MA, USA : Kluwer Academic Publishers Hingham. - 1386-145X .- 1573-1413. ; 14:4, s. 295-319
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Tidskriftsartikel (refereegranskat)abstract
- Pervasive environments are characterized by a large number of embedded devices offering their services to the user .Which of the available services are of most interest to the user considerably depends on the user’s current context. User context is often rich and very dynamic; making an explicit, user-driven discovery of services impractical. Users in such environments would instead like to be continuously informed about services relevant to them. Implicit discovery requests triggered by changes in the context are therefore prevalent. This paper proposes a proactiveservice discovery approach for pervasive environments addressing these implicit requests. Services and user preferences are described by a formal context modelcalled Hyperspace Analogue to Context, which effectively captures the dynamics of context and the relationship between services and context. Based on the model, we propose a set of algorithms that can continuously present the most relevant services to the user in response to changes of context, services or user preferences. Numeric coding methods are applied to improve the algorithms’ performance. The algorithms are grounded in a context-driven service discovery system that automatically reacts to changes in the environment. New context sources and services can be dynamically integrated into the system. A client for smart phones continuously informs users about the discovery results. Experiments show, that the system can efficiently provide the user with continuous, up-to-date information about the most useful services in real time.
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